Merge pull request #1258 from nicholas-leonard/container-fix

parameters() uses torch.type instead of type

Bilinear.lua

@@ -2,7 +2,7 @@ local Bilinear, parent = torch.class('nn.Bilinear', 'nn.Module')
 
 local function isint(x) return type(x) == 'number' and x == math.floor(x) end
 function Bilinear:__assertInput(input)
-   assert(input and type(input) == 'table' and #input == 2,
+   assert(input and torch.type(input) == 'table' and #input == 2,
       'input should be a table containing two data Tensors')
    assert(input[1]:nDimension() == 2 and input[2]:nDimension() == 2,
       'input Tensors should be two-dimensional')

Container.lua

@@ -105,7 +105,7 @@ end
 
 function Container:parameters()
     local function tinsert(to, from)
-        if type(from) == 'table' then
+        if torch.type(from) == 'table' then
             for i=1,#from do
                 tinsert(to,from[i])
             end
@@ -131,7 +131,7 @@ function Container:clearState()
       if self[f] then
          if torch.isTensor(self[f]) then
             self[f] = self[f].new()
-         elseif type(self[f]) == 'table' then
+         elseif torch.type(self[f]) == 'table' then
             self[f] = {}
          else
             self[f] = nil

DontCast.lua

@@ -19,7 +19,7 @@ local function recursiveTypeCopy(dst, src, type_str)
 end
 
 local function tableTensorType(src)
-   if type(src) == 'table' then
+   if type(src) == 'table' then -- Note: don't use torch.type here
       local type_str, found
       for k,v in pairs(src) do
          type_str, found = tableTensorType(v)

FlattenTable.lua

@@ -12,7 +12,7 @@ end
 local function flatten(output, input)
   local input_map  -- has the same structure as input, but stores the
                    -- indices to the corresponding output
-  if type(input) == 'table' then
+  if torch.type(input) == 'table' then
     input_map = {}
     -- forward DFS order
     for i = 1, #input do
@@ -30,8 +30,8 @@ local function checkMapping(output, input, input_map)
   if input_map == nil or output == nil or input == nil then
     return false
   end
-  if type(input) == 'table' then
-    if type(input_map) ~= 'table' then
+  if torch.type(input) == 'table' then
+    if torch.type(input_map) ~= 'table' then
       return false
     end
     if #input ~= #input_map then
@@ -46,7 +46,7 @@ local function checkMapping(output, input, input_map)
     end
     return true
   else
-    if type(input_map) ~= 'number' then
+    if torch.type(input_map) ~= 'number' then
       return false
     end
     return output[input_map] == input
@@ -56,7 +56,7 @@ end
 -- During BPROP we have to build a gradInput with the same shape as the
 -- input.  This is a recursive function to build up a gradInput
 local function inverseFlatten(gradOutput, input_map)
-  if type(input_map) == 'table' then
+  if torch.type(input_map) == 'table' then
     local gradInput = {}
     for i = 1, #input_map do
       gradInput[#gradInput + 1] = inverseFlatten(gradOutput, input_map[i])
@@ -68,7 +68,7 @@ local function inverseFlatten(gradOutput, input_map)
 end
 
 function FlattenTable:updateOutput(input)
-  assert(type(input) == 'table', 'input must be a table')
+  assert(torch.type(input) == 'table', 'input must be a table')
   -- to avoid updating rebuilding the flattened table every updateOutput call
   -- we will do a DFS pass over the existing output table and the inputs to
   -- see if it needs to be rebuilt.
@@ -80,8 +80,8 @@ function FlattenTable:updateOutput(input)
 end
 
 function FlattenTable:updateGradInput(input, gradOutput)
-  assert(type(input) == 'table', 'input must be a table')
-  assert(type(input) == 'table', 'gradOutput must be a table')
+  assert(torch.type(input) == 'table', 'input must be a table')
+  assert(torch.type(input) == 'table', 'gradOutput must be a table')
   -- If the input changes between the updateOutput and updateGradInput call,
   -- then we may have to rebuild the input_map!  However, let's assume that
   -- the input_map is valid and that forward has already been called.

Identity.lua

@@ -17,7 +17,7 @@ function Identity:clearState()
       if self[f] then
          if torch.isTensor(self[f]) then
             self[f] = self[f].new()
-         elseif type(self[f]) == 'table' then
+         elseif torch.type(self[f]) == 'table' then
             self[f] = {}
          else
             self[f] = nil

IndexLinear.lua

@@ -73,7 +73,7 @@ function IndexLinear:reset(stdv)
 end
 
 function IndexLinear:reshapeInput(input)
-   assert(type(input) == 'table')
+   assert(torch.type(input) == 'table')
 
    local ninputs = 0
    for _, v in ipairs(input) do
@@ -108,7 +108,7 @@ function IndexLinear:reshapeInput(input)
    --   { torch.LongTensor(size1), torch.LongTensor(size2), ..., torch.LongTensor(sizeN) }, -- batch of keys
    --   { torch.Tensor(size1), torch.Tensor(size2), ..., torch.Tensor(sizeN) }, -- batch of values,
    -- }
-   if type(keys) == 'table' and type(values) == 'table' then
+   if torch.type(keys) == 'table' and torch.type(values) == 'table' then
       lkeys, lvalues = keys, values
       self.isFlat = false
       self.noBatch = false

SparseLinear.lua

@@ -15,7 +15,7 @@ function SparseLinear:__init(inputSize, outputSize, doGradInput)
    self.gradWeight = torch.Tensor(outputSize, inputSize):zero()
    self.gradBias = torch.Tensor(outputSize):zero()
 
-   assert(type(self.doGradInput) == type(true))
+   assert(type(self.doGradInput) == 'boolean')
 
    self.lastInput = nil
    self.sparseUpdate = NO_LAST_INPUT
@@ -39,7 +39,7 @@ function SparseLinear:reset(stdv)
 end
 
 function SparseLinear:reshapeInput(input)
-   if type(input) == 'table' then
+   if torch.type(input) == 'table' then
       return input, true, false
    else
       if input:dim() == 2 then
@@ -57,7 +57,7 @@ function SparseLinear:updateOutput(input)
    local input, batchMode, legacyMode = self:reshapeInput(input)
    self.legacyMode = legacyMode
 
-   if legacyMode then 
+   if legacyMode then
       input.THNN.SparseLinear_legacyUpdateOutput(
          input:cdata(),
          self.output:cdata(),
@@ -149,8 +149,8 @@ function SparseLinear:accGradParameters(input, gradOutput, scale)
 end
 
 function SparseLinear:updateGradInput(input, gradOutput)
-   if self.legacyMode then 
-      if type(self.gradInput) ~= type(gradOutput) then self.gradInput = gradOutput.new() end
+   if self.legacyMode then
+      if torch.type(self.gradInput) ~= torch.type(gradOutput) then self.gradInput = gradOutput.new() end
       self.gradInput:resizeAs(input)
    else
       self.gradInput = {}
@@ -185,7 +185,7 @@ function SparseLinear:updateGradInput(input, gradOutput)
    return self.gradInput
 end
 
--- These functions do sparse updates / zeros. However, if we accumulated 
+-- These functions do sparse updates / zeros. However, if we accumulated
 -- gradients multiple times, we can't depend on the last input to do sparse
 -- updates.
 function SparseLinear:updateParameters(learningRate)

SpatialFullConvolution.lua

@@ -72,7 +72,7 @@ function SpatialFullConvolution:updateOutput(input)
 
   -- The input can be a table where the second element indicates the target
   -- output size, in which case the adj factors are computed automatically
-  if type(inputTensor) == 'table' then
+  if torch.type(inputTensor) == 'table' then
     inputTensor = input[1]
     local targetTensor = input[2]
     local tDims = targetTensor:dim()
@@ -113,7 +113,7 @@ function SpatialFullConvolution:updateGradInput(input, gradOutput)
 
     -- The input can be a table where the second element indicates the target
     -- output size, in which case the adj factors are computed automatically
-    if type(inputTensor) == 'table' then
+    if torch.type(inputTensor) == 'table' then
       inputTensor = input[1]
       local targetTensor = input[2]
       local tDims = targetTensor:dim()
@@ -122,7 +122,7 @@ function SpatialFullConvolution:updateGradInput(input, gradOutput)
       adjW = calculateAdj(tW, self.kW, self.padW, self.dW)
       adjH = calculateAdj(tH, self.kH, self.padH, self.dH)
       -- Momentarily extract the gradInput tensor
-      if type(self.gradInput) == 'table' then
+      if torch.type(self.gradInput) == 'table' then
         self.gradInput = self.gradInput[1] or inputTensor.new()
       end
     end
@@ -139,7 +139,7 @@ function SpatialFullConvolution:updateGradInput(input, gradOutput)
       adjW, adjH
     )
 
-    if type(input) == 'table' then
+    if torch.type(input) == 'table' then
      -- Create a zero tensor to be expanded and used as gradInput[2].
       self.zeroScalar = self.zeroScalar or input[2].new(1):zero()
       self.ones:resize(input[2]:dim()):fill(1)
@@ -162,7 +162,7 @@ function SpatialFullConvolution:accGradParameters(input, gradOutput, scale)
 
   -- The input can be a table where the second element indicates the target
   -- output size, in which case the adj factors are computed automatically
-  if type(inputTensor) == 'table' then
+  if torch.type(inputTensor) == 'table' then
     inputTensor = input[1]
     local targetTensor = input[2]
     local tDims = targetTensor:dim()

VolumetricFullConvolution.lua

@@ -93,7 +93,7 @@ function VolumetricFullConvolution:updateOutput(input)
 
   -- The input can be a table where the second element indicates the target
   -- output size, in which case the adj factors are computed automatically
-  if type(inputTensor) == 'table' then
+  if torch.type(inputTensor) == 'table' then
     inputTensor = input[1]
     local targetTensor = input[2]
     local tDims = targetTensor:dim()
@@ -128,7 +128,7 @@ function VolumetricFullConvolution:updateGradInput(input, gradOutput)
 
     -- The input can be a table where the second element indicates the target
     -- output size, in which case the adj factors are computed automatically
-    if type(inputTensor) == 'table' then
+    if torch.type(inputTensor) == 'table' then
       inputTensor = input[1]
       local targetTensor = input[2]
       local tDims = targetTensor:dim()
@@ -139,7 +139,7 @@ function VolumetricFullConvolution:updateGradInput(input, gradOutput)
       adjW = calculateAdj(tW, self.kW, self.padW, self.dW)
       adjH = calculateAdj(tH, self.kH, self.padH, self.dH)
       -- Momentarily extract the gradInput tensor
-      if type(self.gradInput) == 'table' then
+      if torch.type(self.gradInput) == 'table' then
         self.gradInput = self.gradInput[1]
       end
     end
@@ -156,7 +156,7 @@ function VolumetricFullConvolution:updateGradInput(input, gradOutput)
       adjT, adjW, adjH
    )
 
-    if type(input) == 'table' then
+    if torch.type(input) == 'table' then
      -- Create a zero tensor to be expanded and used as gradInput[2].
       self.zeroScalar = self.zeroScalar or input[2].new(1):zero()
       self.ones:resize(input[2]:dim()):fill(1)
@@ -177,7 +177,7 @@ function VolumetricFullConvolution:accGradParameters(input, gradOutput, scale)
 
   -- The input can be a table where the second element indicates the target
   -- output size, in which case the adj factors are computed automatically
-  if type(inputTensor) == 'table' then
+  if torch.type(inputTensor) == 'table' then
     inputTensor = input[1]
     local targetTensor = input[2]
     local tDims = targetTensor:dim()

hessian.lua

@@ -216,7 +216,7 @@ function nn.hessian.enable()
    function nn.SpatialConvolution.initDiagHessianParameters(self)
       initDiagHessianParameters(self,{'gradWeight','gradBias'},{'diagHessianWeight','diagHessianBias'})
    end
-   
+
    ----------------------------------------------------------------------
    -- SpatialConvolutionLocal
    ----------------------------------------------------------------------
@@ -361,7 +361,7 @@ function nn.hessian.enable()
 
    function nn.Sequential.parameters(self)
       local function tinsert(to, from)
-         if type(from) == 'table' then
+         if torch.type(from) == 'table' then
             for i=1,#from do
                tinsert(to,from[i])
             end

utils.lua

@@ -158,7 +158,7 @@ function nn.utils.addSingletonDimension(...)
   else
     view, t, dim = select(1,...)
     assert(torch.isTensor(view),
-           "output tensor expected, got " .. type(view))
+           "output tensor expected, got " .. torch.type(view))
   end
 
   assert(torch.isTensor(t), "input tensor expected")
@@ -202,14 +202,14 @@ end
 -- nn.utils.clearState(self, '_buffer', '_buffer2')
 function nn.utils.clear(self, ...)
    local arg = {...}
-   if #arg > 0 and type(arg[1]) == 'table' then
+   if #arg > 0 and torch.type(arg[1]) == 'table' then
       arg = arg[1]
    end
    local function clear(f)
       if self[f] then
          if torch.isTensor(self[f]) then
             self[f]:set()
-         elseif type(self[f]) == 'table' then
+         elseif torch.type(self[f]) == 'table' then
             self[f] = {}
          else
             self[f] = nil